无人机群辅助边缘计算系统的任务卸载和资源分配联合优化

doi:10.12305/j.issn.1001-506X.2024.02.39

系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (2): 751-760.doi: 10.12305/j.issn.1001-506X.2024.02.39

• 通信与网络 • 上一篇

无人机群辅助边缘计算系统的任务卸载和资源分配联合优化

刘世豪¹, 黄仰超¹, 胡航¹^,*, 司江勃², 韩蕙竹¹, 安琪¹

1. 空军工程大学信息与导航学院, 陕西西安 710077
2. 西安电子科技大学综合业务网理论及关键技术国家重点实验室, 陕西西安 710071

收稿日期:2022-10-27 出版日期:2024-01-25 发布日期:2024-02-06
通讯作者: 胡航
作者简介:刘世豪(1999—), 男, 硕士研究生, 主要研究方向为无人机辅助边缘计算
黄仰超(1977—), 男, 副教授, 博士, 主要研究方向为软件无线电、认知无线电、数据链技术
胡航(1989—), 男, 副教授, 博士, 主要研究方向为认知无人机网络、绿色通信、认知数据链
司江勃(1980—), 男, 教授, 博士, 主要研究方向为数据链、认知抗干扰
韩蕙竹(1995—), 女, 硕士研究生, 主要研究方向为认知无人机网络、短包通信
安琪(1994—), 女, 硕士研究生, 主要研究方向为认知无人机网络、无人机中继
基金资助:
国家自然科学基金(61901509);空军工程大学校长基金(XZJK2019033)

Joint optimization of task offloading and resource allocation for UAV swarm-assisted edge computing systems

Shihao LIU¹, Yangchao HUANG¹, Hang HU¹^,*, Jiangbo SI², Huizhu HAN¹, Qi AN¹

1. College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
2. State Key Laboratory of Integrated Services Network, Xidian University, Xi'an 710071, China

Received:2022-10-27 Online:2024-01-25 Published:2024-02-06
Contact: Hang HU

摘要/Abstract

摘要：

为提升无人机群辅助边缘计算系统在负载不均衡场景下的性能, 构建了一种新的无人机群边缘计算系统, 利用无人机之间卸载数据来提高计算资源的利用率, 通过联合优化多架无人机的卸载方案、部署和资源分配, 使得系统的时延和能耗加权和最小。该问题高度非凸, 为此提出一种高效的双层优化算法——启发最优评价算法, 上层使用粒子群算法优化无人机位置, 下层在确定位置的情况下使用块坐标下降算法优化无人机的数据卸载和资源分配。仿真结果表明, 所提方案可有效降低系统成本, 与基准策略相比优势明显。

关键词: 边缘计算, 无人机群, 任务卸载, 资源分配, 位置优化

Abstract:

To improve the performance of unmanned aerial vehicle swarm-assisted edge computing systems under load imbalancing scenarios, a new unmanned aerial vehicle swarm edge computing system is constructed to improve the utilization of computing resources by using offloading data among unmanned aerial vehicle. The weighted sum of the system's delay and energy consumption are minimized by jointly optimizing the offloading scheme, deployment, and resource allocation of multiple unmanned aerial vehicle. The problem is highly nonconvex, and a two-layer optimization scheme is proposed to solve the problem, i.e., the heuristic optimal evaluation algorithm. The upper layer uses a particle swarm algorithm to optimize the unmanned aerial vehicle locations. The lower layer uses a block coordinate descent algorithm to optimize the unmanned aerial vehicle data offloading and resource allocation under the determined locations. The simulation results show that the proposed scheme can effectively reduce the system cost and has obvious advantages over the benchmark strategies.

Key words: edge computing, unmanned aerial vehicle swarm, task offloading, resource allocation, location optimization

中图分类号:

TN925

刘世豪, 黄仰超, 胡航, 司江勃, 韩蕙竹, 安琪. 无人机群辅助边缘计算系统的任务卸载和资源分配联合优化[J]. 系统工程与电子技术, 2024, 46(2): 751-760.

Shihao LIU, Yangchao HUANG, Hang HU, Jiangbo SI, Huizhu HAN, Qi AN. Joint optimization of task offloading and resource allocation for UAV swarm-assisted edge computing systems[J]. Systems Engineering and Electronics, 2024, 46(2): 751-760.

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参考文献 34

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参数	取值	参数	取值
H/m	100	p_i^u/W	0.1
α₀/dBm	-90	C	100
K^[31]	10^－28	B_u/MHz	0.5
p^u/W	0.1	B_i/MHz	0.5
ρ	5	R	4

优化后的无人机位置	系统成本	随机分布的无人机位置/m	系统成本	节省成本/%
(907.9, 898.7, 100)m, (1 361.8, 707.5, 100)m(361.5, 1681.9, 100)m, (1 228.5, 1 541.1, 100)m	201.8	(22, 510, 100), (1 380, 58, 100), (622, 1 154, 100), (1 079, 1 217, 100)	231.9	13.0
		(340, 381, 100), (1 981, 3, 100), (949, 1 558, 100), (1 403, 1 875, 100)	232.8	13.3
		(0, 0, 100), (1 000, 0, 100), (0, 2 000, 100), (2 000, 2 000, 100)	236.2	14.6
		(0, 0, 100), (2 000, 0, 100), (0, 2 000, 100), (2 000, 2 000, 100)	240.7	16.2
		(776, 147, 100), (1 775, 277, 100), (363, 1 864, 100), (1 684, 1 066, 100)	234.1	13.8

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无人机群辅助边缘计算系统的任务卸载和资源分配联合优化

Joint optimization of task offloading and resource allocation for UAV swarm-assisted edge computing systems

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